INTRODUCTION

The CITI-DailyActivities 3D dataset comprises action videos of three modalities such as RGB videos, depth maps, and 3D skeleton structures. It contains fifteen daily activities including walk, sit down, sit still, use a TV remote, stand up, stand still, pick up books, carry books, put down books, carry a backpack, drop a backpack, make a phone call, drink water, wave hand, and clap, as shown in the below Figure.

Figure. one example from each of the fifteen daily activities included in this dataset.

The dataset has 481 sequences. Among them, 181 sequences contain outlier frames presenting in arbitrary locations and lasting for various durations. Ten actors, including eight males and two females, were recruited for building this dataset, and one of them is left-handed. Each activity is performed by each actor between two and five times. A Microsoft Kinect was used for the collection so that the RGB video, the depth maps, and the inferred skeletons of each activity sequence are all available. The skeleton structures in this work were extracted by using the Kinect for Windows SDK v1.8

*we provide various data formats for the action labels, and skeletal features in our dataset such as ".mat", ".txt", and ",npy"

Challenges

Several challenge examples in the skeleton streams in this dataset are shown in the following videos, where the portions of the skeletons extracted with low confidence are drawn in yellow.

Code and Dataset

• Datasets and loader script
• Skeletal joint Locations [.txt]
• Normalized skeletal data (all the skeletal streams are with equal length) [.mat] [.npy]
• Labels [.mat] [.txt] [.npy]

NOTE: The dataset contains 482 action examples, where action example #1 - #300 are the actions without outlier frames, and action example # 301 - # 481 are the actions with outlier frames.

Citation

If you make use of our CITI-DailyActivities 3D dataset in any form, please cite the following reference.

@article{lin2017recognizing,

  title={Recognizing human actions with outlier frames by observation filtering and completion},

  author={Lin, Shih-Yao and Lin, Yen-Yu and Chen, Chu-Song and Hung, Yi-Ping},

  journal={ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)},

  volume={13},

  number={3},

  pages={28},

  year={2017},

  publisher={ACM}

}